{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lKVCOxZOqjuP"
   },
   "outputs": [],
   "source": [
    "# Alpha_RNNs-Bitcoin\n",
    "# Author: Matthew Dixon\n",
    "# Version: 1.1 (27.2.2020)\n",
    "# License: MIT\n",
    "# Email: matthew.dixon@iit.edu\n",
    "# Notes: tested on Mac OS X running Python 3.6.9 with the following packages:\n",
    "# tensorflow=2.0.0, keras=2.3.1, scikit-learn=0.22.1, numpy=1.18.1, matplotlib=3.1.3, pandas=1.0.3, statsmodels=0.10.1\n",
    "# Citation: Please cite the following reference if this notebook is used for research purposes:\n",
    "# Bilokon P., Dixon M.F. and Halperin I., Machine Learning in Finance: From Theory to Practice, Springer Graduate Textbook Series, 2020. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qTkRUh4Nqjuk"
   },
   "source": [
    "## An Introduction to Prediction with RNNs\n",
    "\n",
    "### Overview\n",
    "- This notebook provides an example of how Keras can be used to train and test TensorFlow RNNs for time series prediction. The example dataset is for predicting from noisy, non-stationary data.\n",
    "- Statistical methods used for autoregressive models shall be used to identify the sequence length needed in the RNN and to diagnose the model error.\n",
    "- Plain RNNs are not suited to non-stationary time series modeling. We can use a GRU or LSTM to model non-stationary data, since these models exhibit dynamic auto-correlation structure.\n",
    "- Unlike classical time series methods, e.g. ARIMA, there are no parametric assumptions on the distribution of the errors, and non-linear relationships between response and predictors can be captured. \n",
    "- The data is snapshots of the USD value of Coinbase every minute over 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gLVqutsCqjut"
   },
   "source": [
    "#### Statistician's note\n",
    "- We choose to build a model which provides strong predictive power, at the expense of reduced explanatory power. \n",
    "- Our choice to use a recurrent neural network is predicated on each observation in the time series being dependent on previous observations. The ordering of the observations therefore matters and $X$ is not iid.\n",
    "- Once the input data is appropriately scaled, model building starts with 'feature selection' - identifying the relevant features to include in the model. \n",
    "\n",
    "- In this notebook, we assume that we've already identifed the relevant set of features (i.e. there is only one time series provided).\n",
    "- Our primary concern is assessing the extent to which the model is over-fitting, by comparing the in- and out-of-sample MSEs.\n",
    "\n",
    "#### Implementation notes\n",
    "- It is important to ensure that `shuffle=False` in the fit function, otherwise the ordering of sequences is not preserved. This is especially important for methods which have memory beyond the current sequence (i.e. all methods except RNNs).\n",
    "- Time series cross-validation must be used for hyper-parameter tuning because the ordering of the data matters. In particular, the model must never use training data more recent than the forecasting observation date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JhA4PNWxqjuw"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import tensorflow as tf\n",
    "from datetime import timedelta\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold, TimeSeriesSplit, GridSearchCV\n",
    "\n",
    "import keras.initializers\n",
    "from keras.layers import Dense, Layer, LSTM, GRU, SimpleRNN, RNN\n",
    "from keras.models import Sequential\n",
    "from keras.models import load_model\n",
    "from keras.regularizers import l1, l2\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "# note that the directory containing these two .py's must be in the path variable:\n",
    "from alphaRNN import AlphaRNN\n",
    "from alphatRNN import AlphatRNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "exs-mpLQqju7"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g5QbSSsmqjvF"
   },
   "source": [
    "### Example Data\n",
    "- The example dataset $X$ is a chronologically ordered time series. The ordering of the observations matters and each observation is not assumed to be independent (as with cross-sectional classification data). \n",
    "\n",
    "- Each observation in $X$ has one variable (a.k.a. univariate time series)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ojxc_gL-qjvJ"
   },
   "source": [
    "Loading the data into a Pandas Dataframe, then viewing the first ten observations and the distribution of the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u1OUq236qjvN",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/coinbase.csv', index_col=1)\n",
    "\n",
    "df.index = pd.to_datetime(df.index, infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains missing values; in order to prevent this causing errors, we replace these with adjacent values from the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1662 observations are missing.\n",
      "This is 0.368% of the total.\n"
     ]
    }
   ],
   "source": [
    "nof_missing_values = sum(np.isnan(df['USD']))\n",
    "\n",
    "print(nof_missing_values, 'observations are missing.')\n",
    "print('This is {:.3f}% of the total.'.format(nof_missing_values*100/len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "gUXzLXEoqjv5",
    "outputId": "9b519613-55f8-44e7-d189-df14e481643a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now 0 observations are missing.\n"
     ]
    }
   ],
   "source": [
    "df = df.fillna(method=\"backfill\")\n",
    "\n",
    "nof_missing_values = sum(np.isnan(df['USD']))\n",
    "\n",
    "print('Now', nof_missing_values, 'observations are missing.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xp8DNh3Lqjvj"
   },
   "source": [
    "# RNN Regression\n",
    "We consider a univariate prediction problem where the time series is given by 'USD' in the data frame, and for each input sequence we predict the value 4 time-steps into the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AruP1dVpqjvm"
   },
   "outputs": [],
   "source": [
    "use_features = ['USD'] # continuous input\n",
    "target = ['USD'] # continuous output\n",
    "n_steps_ahead = 4 # forecasting horizon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HgFn0_Zhqjvy"
   },
   "source": [
    "### Stationarity\n",
    "It is essential to determine whether the time series is \"stationary\". Informally, stationarity is when the auto-covariance is independent of time. Failure to establish stationarity will almost certainly lead to misinterpretation of model identification and diagnostic tests. Moreover, stationarity is decisive in characterizing the prediction problem and whether to use a more advanced architecture. In particular, we can expect a plain RNN to perform poorly if the data is non-stationary as the RNN exhibits fixed auto-covariance. \n",
    "\n",
    "We perform an Augmented Dickey-Fuller test to establish stationarity under the assumption that the time series has a constant bias but does not exhibit a time trend. In other words, we assume that the time series is already de-trended. \n",
    "\n",
    "If the stationarity test fails, even after first de-trending the time series, then one potential recourse is to simply take differences of time series and predict $\\Delta y_t$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "azMbPKuXqjv1"
   },
   "source": [
    "The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the p-value is above $(1-\\alpha)$, then we cannot reject that there is a unit root. Note that a subset of the time series is used to reduce the memory requirements of the test. We use the first 200,000 samples to test for stationarity. While the test statistic is sensitive to the data size, the ADF test is always accepted at the 99\\% level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V_DF3IABqjwA"
   },
   "outputs": [],
   "source": [
    "sample = df['USD'][:200000]\n",
    "adf, p, usedlag, nobs, cvs, aic = sm.tsa.stattools.adfuller(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wLjxzajyqjwJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF: -1.628446514215208\n",
      "p-value: 0.46830397402696317,\n",
      "N: 19953, \n",
      "critical values: {'1%': -3.4306777773505996, '5%': -2.86168486605018, '10%': -2.566847108243414}\n"
     ]
    }
   ],
   "source": [
    "adf_results_string = 'ADF: {}\\np-value: {},\\nN: {}, \\ncritical values: {}'\n",
    "print(adf_results_string.format(adf, p, nobs, cvs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jxx3NIqxqjwT"
   },
   "source": [
    "Here we accept the null as the p-value is larger than 0.01, thus we can not reject the test at the 99% confidence level. This suggests that the time series is **non-stationary**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6Vo_X2UDqjwV"
   },
   "source": [
    "#### Autoregressive Model Identification: The partial auto-correlation\n",
    "It is important to determine the number of lags, the sequence length, required in the RNN by statistical analysis. A brute-force approach will in general be too time-consuming.\n",
    "\n",
    "A partial auto-correlation at lag $h\\geq 2$ is a conditional auto-correlation between a variable, $X_t$, and its $h^{th}$ lag, $X_{t-h}$ under the assumption that we control for the values of the intermediate lags, $X_{t-1},\\dots, X_{t-h+1}$:\n",
    "\n",
    "$$\\begin{align}\\tau_h&:=\\tau(X_t, X_{t-h}; X_{t-1},\\dots, X_{t-h+1})\\\\\n",
    "&:=\\frac{\\gamma(X_t, X_{t-h}; X_{t-1},\\dots, X_{t-h+1})}{\\sqrt{\\gamma(X_t |X_{t-1},\\dots, X_{t-h+1})\\gamma(X_{t-h} |X_{t-1},\\dots, X_{t-h+1}))}},\n",
    "\\end{align}$$\n",
    "where $\\gamma_h:=\\gamma(X_tX_{t-h})$ is the lag-$h$ autocovariance. The partial autocorrelation function $\\tau_h:\\mathbb{N} \\rightarrow [-1,1]$ is a map $h:\\mapsto \\tau_h$.\n",
    "\n",
    "The estimated partial auto-correlation function (PACF) can be used to identify the order of an autoregressive time series model. Values of $|\\tau_h|$ greater or equal to $\\frac{\\Phi^{-1}(\\alpha)}{\\sqrt{T}}$, where $T$ is the number of observations and $\\Phi(z)$ is the standard normal CDF, are significant lag $h$ partial autocorelations at the $\\alpha$ confidence level.\n",
    "\n",
    "We use the stattools package to estimate the PACF. The `nlags` parameter is the maximum number of lags used for PACF estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MKLHw7tqqjwY"
   },
   "outputs": [],
   "source": [
    "pacf = sm.tsa.stattools.pacf(df[use_features], nlags=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since $\\Phi^{-1}(0.99) \\simeq 2.58$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xzsy_bjyqjwh"
   },
   "outputs": [],
   "source": [
    "T = len(df[use_features])\n",
    "\n",
    "sig_test = lambda tau_h: np.abs(tau_h) > 2.58/np.sqrt(T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RsQTxSq5qjwf"
   },
   "source": [
    "We find the first lag which isn't significant at the 99% level and automatically determine the number of lags needed in our autoregressive model as one below this value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xzsy_bjyqjwh"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_steps set to 4\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(pacf)):\n",
    "    if sig_test(pacf[i]) == False:\n",
    "        n_steps = i - 1\n",
    "        print('n_steps set to', n_steps)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fnv19w5tqjwy"
   },
   "source": [
    "This may lead to a high order model, with more lags than strictly necessary. We could view this value, informally, as an upper bound on the number of lags needed. We can also simply identify the order of the model based on the plot of the PACF. In this case, a minimum of 4 lags appears satisfactory, although more may be needed. Unlike autoregressive models, the advantage of using fewer parameters is purely computational as adding more lags does not increase the number of parameters, only the size of the tensorial representation of the sequence data in TensorFlow. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "2YHK-wIIqjw1",
    "outputId": "f8c02cc7-2422-4a9e-9c80-1a641347fc54"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pacf, label='pacf')\n",
    "plt.plot([2.58/np.sqrt(T)]*30, label='99% confidence interval (upper)')\n",
    "plt.plot([-2.58/np.sqrt(T)]*30, label='99% confidence interval (lower)')\n",
    "plt.xlabel('number of lags')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jHSnkymxqjw7"
   },
   "source": [
    "### Splitting the time series into training and testing sets\n",
    "Split the training and test set by using the first 80% of the time series and the remaining 20% for the test set. Note that the test set must be in the future of the training set to avoid look-ahead bias. Also, random sampling of the data can not be used as this would eliminate the auto-correlation structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y7D1y9USqjw9"
   },
   "outputs": [],
   "source": [
    "train_weight = 0.8\n",
    "split = int(len(df) * train_weight)\n",
    "\n",
    "df_train = df[use_features].iloc[:split]\n",
    "df_test = df[use_features].iloc[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "JB3yo5YkqjxE"
   },
   "source": [
    "\n",
    "### Scaling\n",
    "Standardization of the data is important to avoid potential scaling difficulties in the fitting of the model. When there is more than one feature (covariate), scaling avoids one feature dominating over another due to disparate scales.\n",
    "\n",
    "To avoid introducing a look-ahead bias into the prediction, we must re-scale the training data without knowledge of the test set. Hence, we will simply standardize the training set using the mean and standard deviation of the training set and not the whole time series. Additionally, to avoid introducing a systematic bias into test set, we use the identical normalization for the test set - the mean and standard deviation of the training set are used to normalize the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JxQ09iVxqjxL"
   },
   "outputs": [],
   "source": [
    "# note that for a multivariate time series, you would need to scale \n",
    "# each variable by its own mean and standard deviation in the training set\n",
    "mu = np.float(df_train.mean())\n",
    "sigma = np.float(df_train.std())\n",
    "\n",
    "stdize_input = lambda x: (x - mu) / sigma\n",
    "\n",
    "df_train = df_train.apply(stdize_input)\n",
    "df_test = df_test.apply(stdize_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ooDbr1Giqjxa"
   },
   "source": [
    "### Data formatting for RNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "80KLxffJqjxd"
   },
   "source": [
    "TensorFlow uses tensors to represent data. To perform sequence learning, the time series of variables must be transformed to a series of over-lapping sub-sequences. \n",
    "\n",
    "For example, consider a univariate time series of increasing integers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fk_D3SrKqjxe"
   },
   "source": [
    "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "avKQL4MWqjxg"
   },
   "source": [
    "Setting the sequence length to 10, for example, we move the window forward by one observation at a time and construct new sequences:\n",
    "\n",
    "1 2 3 4 5 6 7 8 9 10\n",
    "\n",
    "2 3 4 5 6 7 8 9 10 11\n",
    "\n",
    "3 4 5 6 7 8 9 10 11 12\n",
    "\n",
    "4 5 6 7 8 9 10 11 12 13\n",
    "\n",
    "5 6 7 8 9 10 11 12 13 14\n",
    "\n",
    "6 7 8 9 10 11 12 13 14 15\n",
    "\n",
    "\n",
    "Let's define the following function for reshaping the data into one-step ahead times series prediction format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YPCndJ8sqjxj"
   },
   "outputs": [],
   "source": [
    "def get_lagged_features(df, n_steps, n_steps_ahead):\n",
    "    \"\"\"\n",
    "    df: pandas DataFrame of time series to be lagged\n",
    "    n_steps: number of lags, i.e. sequence length\n",
    "    n_steps_ahead: forecasting horizon\n",
    "    \"\"\"\n",
    "    lag_list = []\n",
    "    \n",
    "    for lag in range(n_steps + n_steps_ahead - 1, n_steps_ahead - 1, -1):\n",
    "        lag_list.append(df.shift(lag))\n",
    "    lag_array = np.dstack([i[n_steps+n_steps_ahead-1:] for i in lag_list])\n",
    "    # We swap the last two dimensions so each slice along the first dimension\n",
    "    # is the same shape as the corresponding segment of the input time series \n",
    "    lag_array = np.swapaxes(lag_array, 1, -1)\n",
    "    return lag_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gOk1scB1qjxn"
   },
   "source": [
    "We shall first transform the training input and output data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_train = get_lagged_features(df_train, n_steps, n_steps_ahead)\n",
    "y_train =  df_train.values[n_steps + n_steps_ahead - 1:]\n",
    "y_train_timestamps = df_train.index[n_steps + n_steps_ahead - 1:]\n",
    "\n",
    "x_test = get_lagged_features(df_test, n_steps, n_steps_ahead)\n",
    "y_test =  df_test.values[n_steps + n_steps_ahead - 1:]\n",
    "y_test_timestamps = df_test.index[n_steps + n_steps_ahead - 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "av-Dyaweqjxw"
   },
   "source": [
    "Print the shapes of each tensor. The first digit is the number of observations. For feature arrays, the second digit is the sequence length (i.e. the number of lags in the model) and the final digit is the dimension of each element in the sequence or output vector respectively. In this case, the example performs univariate time series analysis and so the dimension of the input and output is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(361337, 4, 1), (361337, 1), (90330, 4, 1), (90330, 1)]\n"
     ]
    }
   ],
   "source": [
    "print([tensor.shape for tensor in (x_train, y_train, x_test, y_test)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aieEXctCqjyK"
   },
   "source": [
    "### Model Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tBFnFLZZqjyM"
   },
   "outputs": [],
   "source": [
    "def AlphatRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphatRNN(n_units, activation='tanh', recurrent_activation='sigmoid', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def AlphaRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphaRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True))\n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def SimpleRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(SimpleRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True, stateful=False))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def GRU_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(GRU(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def LSTM_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(LSTM(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True)) \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0Bmr9nNVqjyR"
   },
   "outputs": [],
   "source": [
    "max_epochs = 1000\n",
    "batch_size = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KGCWFF1-qjyW"
   },
   "source": [
    "Use a batch size of 1000 as the dataset is reasonably large and the training time would be too long otherwise. 20 epochs have been used here, but a better approach would be to use a stopping criteria through a call back. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SrDjZpVdqjyZ"
   },
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=100, min_delta=1e-7, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jr_42Ab_qjye"
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "    'rnn': {\n",
    "        'model': None, 'function': SimpleRNN_, 'l1_reg': 0.0, 'H': 20, \n",
    "        'color': 'blue', 'label':'RNN'}, \n",
    "    'alpharnn': {\n",
    "        'model': None, 'function': AlphaRNN_, 'l1_reg': 0.0, 'H': 10, \n",
    "        'color': 'green', 'label': '$\\\\alpha$-RNN' }, \n",
    "    'alphatrnn': {\n",
    "        'model': None, 'function':AlphatRNN_, 'l1_reg': 0.0, 'H': 5, \n",
    "        'color': 'cyan', 'label': '$\\\\alpha_t$-RNN'},\n",
    "    'gru': {\n",
    "        'model': None, 'function':GRU_,'l1_reg': 0.0, 'H': 10, \n",
    "        'color': 'orange', 'label': 'GRU'},\n",
    "    'lstm': {\n",
    "        'model': None, 'function': LSTM_,'l1_reg': 0.0, 'H': 10, \n",
    "        'color':'red', 'label': 'LSTM'}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optionally, load pre-trained models\n",
    "Training the models takes several hours. To save time, you may load the already fitted models instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "do_training = False # Set to True if you wish to train your own models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "if do_training is False:\n",
    "    custom_objects = {'AlphaRNN': AlphaRNN, 'AlphatRNN': AlphatRNN}\n",
    "    for key in params.keys():\n",
    "        params[key]['model'] = load_model('trained-RNNs/RNNs-Bitcoin-' + key + '.hdf5', custom_objects=custom_objects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oKcoWfmHqjyi"
   },
   "source": [
    "# Cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below performs a grid search to optimise the `n_units` and `l1_reg` for each of the models using the training data. \n",
    "\n",
    "The results are cross-validated to avoid over-fitting. Scikit-Learn's `TimeSeriesSplit` function is used to partition the data into 5 pairs of training and testing sets, where the test data is always ahead of the training data in time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dM8rOT-rqjyj"
   },
   "outputs": [],
   "source": [
    "cross_val = False # WARNING: Changing this to True will take many hours to run\n",
    "\n",
    "if do_training and cross_val:\n",
    "    n_units = [5, 10, 20]\n",
    "    l1_reg = [0, 0.001, 0.01, 0.1]\n",
    "    \n",
    "    # A dictionary containing a list of values to be iterated through\n",
    "    # for each parameter of the model included in the search\n",
    "    param_grid = {'n_units': n_units, 'l1_reg': l1_reg}\n",
    "    \n",
    "    # In the kth split, TimeSeriesSplit returns first k folds \n",
    "    # as training set and the (k+1)th fold as test set.\n",
    "    tscv = TimeSeriesSplit(n_splits = 5)\n",
    "    \n",
    "    # A grid search is performed for each of the models, and the parameter set which\n",
    "    # performs best over all the cross-validation splits is saved in the `params` dictionary\n",
    "    for key in params.keys():\n",
    "        print('Performing cross-validation. Model:', key)\n",
    "        model = KerasRegressor(build_fn=params[key]['function'], epochs=max_epochs, \n",
    "                               batch_size=batch_size, verbose=2)\n",
    "        grid = GridSearchCV(estimator=model, param_grid=param_grid, \n",
    "                            cv=tscv, n_jobs=1, verbose=2)\n",
    "        grid_result = grid.fit(x_train, y_train, callbacks=[es])\n",
    "        print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "        \n",
    "        means = grid_result.cv_results_['mean_test_score']\n",
    "        stds = grid_result.cv_results_['std_test_score']\n",
    "        params_ = grid_result.cv_results_['params']\n",
    "        for mean, stdev, param_ in zip(means, stds, params_):\n",
    "            print(\"%f (%f) with %r\" % (mean, stdev, param_))\n",
    "            \n",
    "        params[key]['H'] = grid_result.best_params_['n_units']\n",
    "        params[key]['l1_reg']= grid_result.best_params_['l1_reg']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BqEXCGyoqjyo"
   },
   "source": [
    "# Train cross-validated model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the grid search was performed, the parameters `n_units` and `l1_reg` with the best average performance across the cross-validation splits are used to train the models on the entire training set. If not, the values from the initialisation of `params` above are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A0FpTqNWqjyq",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if do_training is True:\n",
    "    for key in params.keys():\n",
    "        tf.random.set_seed(0)\n",
    "        print('Training', key, 'model')\n",
    "        model = params[key]['function'](params[key]['H'], params[key]['l1_reg'])\n",
    "        model.fit(x_train, y_train, epochs=max_epochs, \n",
    "                  batch_size=batch_size, callbacks=[es], shuffle=False)\n",
    "        params[key]['model'] = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally save the fitted models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tQmA1vK1mbCM"
   },
   "outputs": [],
   "source": [
    "for key in params.keys():\n",
    "    params[key]['model'].save('RNNs-Bitcoin-SAVED-' + key + '.hdf5', overwrite=True)  # creates a HDF5 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print out the value of $\\alpha \\in [0,1]$ for the alpha-RNN model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a9OE7I07dEhV"
   },
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return (1 / (1 + np.exp(-x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fC3xzTJ4cHG4",
    "outputId": "1e84ce92-289c-4e07-fe25-c3f5b4a01926"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = [0.24113195]\n"
     ]
    }
   ],
   "source": [
    "model = params['alpharnn']['model']\n",
    "names = [weight.name for layer in model.layers for weight in layer.weights]\n",
    "weights = model.get_weights()\n",
    "\n",
    "for name, weight in zip(names, weights):\n",
    "    if 'alpha:0' in name:\n",
    "        print(\"alpha = \" + str(sigmoid(weight)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y1gZjjPgqjy3"
   },
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aEhlyKjSqjy4"
   },
   "source": [
    "We shall now apply the fitted RNN models to the training set and the test set, separately. We can then informally assess the extent of over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "t3a1F-Y8qjy5",
    "outputId": "c183b29d-0f1b-48b0-859d-27e73ac8f919",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn_1 (SimpleRNN)     (None, 20)                440       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 461\n",
      "Trainable params: 461\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "361337/361337 [==============================] - 15s 40us/step\n",
      "90330/90330 [==============================] - 4s 39us/step\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "alpha_rnn_1 (AlphaRNN)       (None, 10)                121       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 132\n",
      "Trainable params: 132\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "361337/361337 [==============================] - 16s 44us/step\n",
      "90330/90330 [==============================] - 4s 43us/step\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "alphat_rnn_1 (AlphatRNN)     (None, 5)                 80        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 86\n",
      "Trainable params: 86\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "361337/361337 [==============================] - 18s 51us/step\n",
      "90330/90330 [==============================] - 4s 49us/step\n",
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, 10)                360       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 371\n",
      "Trainable params: 371\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "361337/361337 [==============================] - 24s 67us/step\n",
      "90330/90330 [==============================] - 6s 63us/step\n",
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 10)                480       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 491\n",
      "Trainable params: 491\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "361337/361337 [==============================] - 24s 67us/step\n",
      "90330/90330 [==============================] - 6s 66us/step\n"
     ]
    }
   ],
   "source": [
    "for key in params.keys():\n",
    "    model = params[key]['model']\n",
    "    model.summary()\n",
    "    \n",
    "    params[key]['pred_train'] = model.predict(x_train, verbose=1)\n",
    "    params[key]['MSE_train'] = mean_squared_error(y_train, params[key]['pred_train'])\n",
    "    \n",
    "    params[key]['pred_test'] = model.predict(x_test, verbose=1) \n",
    "    params[key]['MSE_test'] = mean_squared_error(y_test, params[key]['pred_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I99U7i80qjy-"
   },
   "source": [
    "### Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J_ADoAkeqjzB"
   },
   "source": [
    "Finally assess the performance of the model in and out-of-sample using the MSE. We expect the mean error to be larger on the test set than on the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set: 361337\n",
      "testing set: 90330\n"
     ]
    }
   ],
   "source": [
    "print('training set:', len(y_train))\n",
    "print('testing set:', len(y_test))\n",
    "\n",
    "# Upper limits for `l` & `u` in the cells below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['rnn', 'alpharnn', 'alphatrnn', 'gru', 'lstm'])\n"
     ]
    }
   ],
   "source": [
    "print(params.keys())\n",
    "\n",
    "# Set `compare` in the cells below to a list\n",
    "# containing any subset of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_pts = 10**4\n",
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # lower and upper indices of range to plot \n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling ratio for under `max_pts`\n",
    "                                        # per series.  Set `None` to disable. \n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = y_train_timestamps[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_train'][l:u:ds]\n",
    "    label = params[key]['label'] + ' (train MSE: %.2e)' % params[key]['MSE_train']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "plt.plot(x_vals, y_train[l:u:ds], c=\"black\", label=\"Observed\", lw=1)\n",
    "start, end = x_vals.min(), x_vals.max()\n",
    "xticks =  [start.date() + timedelta(days=(1+i)) for i in range(1 + (end - start).days)]\n",
    "xticks = xticks[::max(1, len(xticks)//30)]\n",
    "for t in xticks: plt.axvline(x=t, c='gray', linewidth=0.5, zorder=0)\n",
    "plt.xticks(xticks, rotation=70)\n",
    "plt.xlim(start, end)\n",
    "plt.ylabel('$\\hat{Y}$', rotation=0, fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Outputs (Training)', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 556
    },
    "colab_type": "code",
    "id": "f67u-snqqjzD",
    "outputId": "ca4f8a64-3780-4cc1-a3f5-b738606f5c77",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # lower and upper indices of range to plot \n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling ratio for under `max_pts`\n",
    "                                        # per series.  Set `None` to disable.\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = y_test_timestamps[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_test'][l:u:ds]\n",
    "    label = params[key]['label'] + ' (test MSE: %.2e)' % params[key]['MSE_test']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "plt.plot(x_vals, y_test[l:u:ds], c=\"black\", label=\"Observed\", lw=1)\n",
    "start, end = x_vals.min(), x_vals.max()\n",
    "xticks =  [start.date() + timedelta(days=(1+i)) for i in range(1 + (end - start).days)]\n",
    "xticks = xticks[::max(1, len(xticks)//30)]\n",
    "for t in xticks: plt.axvline(x=t, c='gray', linewidth=0.5, zorder=0)\n",
    "plt.xticks(xticks, rotation=70)\n",
    "plt.xlim(start, end)\n",
    "plt.ylabel('$\\hat{Y}$', rotation=0, fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Outputs (Testing)', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # e.g. (None, 100000) lower and upper indices of range to plot \n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling ratio for under `max_pts`\n",
    "                                        # per series.  Set `None` to disable.\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = y_test_timestamps[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_test'][l:u:ds] - y_test[l:u:ds]\n",
    "    label = params[key]['label'] + ' (test MSE: %.2e)' % params[key]['MSE_test']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "start, end = x_vals.min(), x_vals.max()\n",
    "xticks =  [start.date() + timedelta(days=(1+i)) for i in range(1 + (end - start).days)]\n",
    "xticks = xticks[::max(1, len(xticks)//30)]\n",
    "plt.axhline(0, linewidth=0.8)\n",
    "for t in xticks: plt.axvline(x=t, c='gray', linewidth=0.5, zorder=0)\n",
    "plt.xticks(xticks, rotation=80)\n",
    "plt.xlim(start, end)\n",
    "plt.ylabel('$\\hat{Y}-Y$', fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Error (Testing)', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "colab_type": "code",
    "id": "XSX5YYRCqjzH",
    "outputId": "99f4a429-9643-4e42-ea49-8efa6bf190df",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # lower and upper indices of range to plot - e.g. (None, 10000)\n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling ratio for under `max_pts`\n",
    "                                        # per series.  Set `None` to disable.\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = y_train_timestamps[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_train'][l:u:ds] - y_train[l:u:ds]\n",
    "    label = params[key]['label'] + ' (train MSE: %.2e)' % params[key]['MSE_train']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "start, end = x_vals.min(), x_vals.max()\n",
    "xticks =  [start.date() + timedelta(days=(1+i)) for i in range(1 + (end - start).days)]\n",
    "xticks = xticks[::max(1, len(xticks)//30)]\n",
    "plt.axhline(0, linewidth=0.8)\n",
    "for t in xticks: plt.axvline(x=t, c='gray', linewidth=0.5, zorder=0)\n",
    "plt.xticks(xticks, rotation=80)\n",
    "plt.xlim(start, end)\n",
    "plt.ylabel('$\\hat{Y}-Y$', fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Error (Training)', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "6c_adfrzqjzM"
   },
   "source": [
    "### Model Diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UGHvu0xVqjzO"
   },
   "source": [
    "A fitted time series model must be examined for underfitting with a white noise test. We analyze the model residuals (i.e. the error $u_t$) to determine whether it is white noise or whether it is auto-correlated. The latter case provides statistical evidence that more lags are needed in the RNN. Box and Pierce propose the Portmanteau statistic:\n",
    "\n",
    "$$Q^*(m)=T\\sum_{l=1}^m\\hat{\\tau}_l^2,$$  \n",
    "as a test statistic for the null hypothesis $H_0:\\tau_1=\\dots=\\tau_m=0$ against the alternative hypothesis $H_a:\\tau_i\\neq 0$ for some $i\\in\\{1,\\dots,m\\}$, where $T$ is the number of observations, $\\hat{\\tau}_i$ are the sample autocorrelations of the residual, and $m$ is the maximum lag used in the test. There are several heuristics in the statistics literature to determine the maximum lag such as the Schwert statistic. \n",
    "\n",
    "The Box-Pierce statistic follows an asymptotically chi-squared distribution with $m$ degrees of freedom.\n",
    "\n",
    "The Ljung-Box test statistic increases the power of the test in finite samples:\n",
    "$$Q(m)=T(T+2)\\sum_{l=1}^m\\frac{\\hat{\\tau}_l^2}{T-l}$$\n",
    "This statistic also follows an asymptotically chi-squared distribution with $m$ degrees of freedom. The decision rule is to reject $H_0$ if $Q(m)>\\chi_{\\alpha}^2$ where $\\chi_{\\alpha}^2$ denotes the $100(1-\\alpha)^{th}$ percentile of a chi-squared distribution with m degrees of freedom and is the significance level for rejecting $H_0$.\n",
    "\n",
    "The test can be time consuming and we select a subset of the residuals. Here we simply set the maximum lag to 20. In the results below, we find that the p-values are all smaller than 0.01, indicating that we can reject the null at the 99% confidence level for any lag. This is strong evidence that the model is under-fitting and more lags are needed in our model. Unlike an auto-regressive model, increasing the number of lags in the RNN does not increase the number of weights. Thus there is no danger of over-fitting by increasing the lag, although there will be an increase in the training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7bpznKjiqjzQ"
   },
   "outputs": [],
   "source": [
    "# number of samples to use for computing test statistic\n",
    "n = 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['rnn', 'alpharnn', 'alphatrnn', 'gru', 'lstm'])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rIPFOuKAqjzV"
   },
   "outputs": [],
   "source": [
    "key = 'alpharnn'\n",
    "predicted = params[key]['pred_test']\n",
    "residual = y_test[-n:] - predicted[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2-radzroqjzb"
   },
   "outputs": [],
   "source": [
    "lb, p = sm.stats.diagnostic.acorr_ljungbox(residual, lags=20, boxpierce=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WWvgE9ouqjzf"
   },
   "source": [
    "The Box-Ljung test statistics are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rn0gmXkDqjzg",
    "outputId": "517efe22-705e-420e-f8ed-e3d4f0180835"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([61562.07460753, 90466.80953156, 98349.37299933, 98710.33220986,\n",
       "       98710.73332643, 98756.13543138, 98854.37711435, 98948.93155159,\n",
       "       98986.97252624, 98987.50348873, 99007.36449068, 99071.19145694,\n",
       "       99135.55844865, 99194.2390851 , 99238.35964502, 99290.24934538,\n",
       "       99360.44600305, 99421.8768069 , 99463.51398864, 99467.77658386])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D0MmBUaDqjzk"
   },
   "source": [
    "The p-values are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TTEIIp-Sqjzn",
    "outputId": "15031561-6612-43b6-99c0-dafa4a5c034e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0.])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "ML_in_Finance-RNNs-Bitcoin.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
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